This journal paper focuses on developing surrogate models for predicting the damage equivalent load (DEL) on wind turbines without needing extensive aeroelastic simulations. The study emphasizes the development of a sequential machine learning architecture for this purpose. The study also explores implementing simplified wake models and transfer learning to enhance the models' prediction capabilities in various wind conditions.

This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.

The rising share of wind power poses challenges to cost-effective integration while ensuring reliability. Balancing the needs of the power system and contributions of wind power is crucial for long-term value. Research should prioritize wind power advantages over competitors, focussing on internal challenges. Collaboration with other technologies is essential for addressing the fundamental objectives of power systems – maintaining reliable supply–demand balance at the lowest cost.

Wake steering can be integrated into wind farm layout optimization through a co-design approach. This study estimates the potential of this method for a wide range of realistic conditions, adopting a tailored genetic algorithm and novel geometric yaw relations. A gain in the annual energy yield between 0.3 % and 0.4 % is obtained for a 16-tubrine farm, and a multi-objective implementation is used to limit loss in the case that wake steering is not used during farm operation.

The performance of a wind turbine is affected by blade surface degradation due to wear and tear, dirt, bugs, and icing. As blades degrade, optimal operating points such as the tip-speed ratio (TSR) can change. Re-tuning the TSR to its new optimal value can lead to recovery of energy losses under blade degradation. In this work, we utilize a real-time algorithm to re-tune the TSR to its new unknown optimal value under blade degradation and demonstrate energy gains using simulations.

Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.

Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.

This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.

This paper presents a fast cycle–power computation model for fixed-wing ground-generation airborne wind energy systems. It is suitable for sensitivity and scalability studies, which makes it a valuable tool for design and innovation trade-offs. It is also suitable for integration with cost models and systems engineering tools, enhancing its applicability in assessing the potential of airborne wind energy in the broader energy system.

Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.

Intentional misalignment of upstream turbines in wind plants in order to steer wakes away from downstream turbines has been a topic of research interest for years but has not yet achieved widespread commercial adoption. We deploy one such wake steering system to a utility-scale wind plant and then create a model to predict plant behavior and enable successful control. We apply calibrations to a physics-based model and use machine learning to correct its outputs to improve predictive capability.